Grabbing SPINS gradients

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Read in the SPINS big table

## New names:
## Rows: 164640 Columns: 8
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): ROI, Network, Subject, Site dbl (4): ...1, grad1, grad2, grad3
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`

read subject data

##  [1] "record_id"                        "scanner"                         
##  [3] "diagnostic_group"                 "demo_sex"                        
##  [5] "demo_age_study_entry"             "scog_rmet_total"                 
##  [7] "scog_er40_total"                  "scog_tasit1_total"               
##  [9] "scog_tasit2_sinc"                 "scog_tasit2_simpsar"             
## [11] "scog_tasit2_parsar"               "scog_tasit3_lie"                 
## [13] "scog_tasit3_sar"                  "np_domain_tscore_process_speed"  
## [15] "np_domain_tscore_att_vigilance"   "np_domain_tscore_work_mem"       
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"
## New names:
## Rows: 467 Columns: 43
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (36): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`
##  [1] "scog_rmet_total"                  "scog_er40_total"                 
##  [3] "scog_tasit1_total"                "scog_tasit2_parsar"              
##  [5] "scog_tasit2_simpsar"              "scog_tasit2_sinc"                
##  [7] "scog_tasit3_lie"                  "scog_tasit3_sar"                 
##  [9] "np_domain_tscore_att_vigilance"   "np_domain_tscore_process_speed"  
## [11] "np_domain_tscore_work_mem"        "np_domain_tscore_verbal_learning"
## [13] "np_domain_tscore_visual_learning" "np_domain_tscore_reasoning_ps"   
## [15] "fd_mean_rest"

Check subject overlap

grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav_ssd$record_id[order(lol_spins_behav_ssd$record_id)]

# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]

complete.cases(spins_grads_wide)
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
complete.cases(lol_spins_behav_ssd)
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
kept.sub <- lol_spins_behav_ssd$record_id[complete.cases(lol_spins_behav_ssd)==TRUE] # 246

## grab the matching data

behav.dat <- lol_spins_behav_ssd[kept.sub,c(6:19)]#, 22:40)]
spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]

## variables to regress out
regout.dat <- var2regout_num[kept.sub,]

Demographics

# lol_demo <- 
#   read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
#   filter(exclude_MRI==FALSE, 
#          exclude_meanFD==FALSE, 
#          exclude_earlyTerm==FALSE) %>% as.data.frame
# lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
# rownames(lol_demo) <- lol_demo$record_id
# lol_demo_match <- lol_demo[kept.sub,]
# 
# spins_demo <- lol_demo_match %>% 
#   select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
#          scog_tasit1_total,
#          scog_tasit2_total, scog_tasit3_total,np_composite_tscore, np_domain_tscore_att_vigilance,
#          np_domain_tscore_process_speed, np_domain_tscore_work_mem,
#          np_domain_tscore_verbal_learning, np_domain_tscore_visual_learning,
#          np_domain_tscore_reasoning_ps, 
#          #bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
#          #fd_mean_rest
#   ) %>% data.frame
# colnames(spins_demo)
# rownames(spins_demo) <- lol_demo_match$subject

sub.dx <- spins_dx_org[kept.sub,]

sub.dx %>%
  group_by(diagnostic_group) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>% t
##                      [,1]      
## diagnostic_group     "case"    
## demo_age_study_entry "31.41532"
sub.dx %>%
  group_by(diagnostic_group) %>%
  summarize_if(is.numeric, sd, na.rm = TRUE) %>% t
##                      [,1]      
## diagnostic_group     "case"    
## demo_age_study_entry "9.768209"
cbind(table(sub.dx$diagnostic_group, sub.dx$demo_sex), table(sub.dx$diagnostic_group))
##      female male    
## case     79  169 248

Regress out the effects

table(regout.dat$demo_sex_num)
## 
##   0   1 
##  79 169
behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)

grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)

grad.reg2plot <- apply(grad.dat, 2, function(x){
  model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
  return(model$residual + model$coefficient[1])
} )

grab some network colours

networks <- read_delim("../networks.txt", 
                       "\t", escape_double = FALSE, trim_ws = TRUE) %>%
  select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
  distinct() %>%
  add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
  mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
  arrange(NETWORKKEY)
## Rows: 718 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
networks$hex <- darken(networks$hex, 0.2)

# oi <- networks$hex
# swatchplot(
#   "-40%" = lighten(oi, 0.4),
#   "-20%" = lighten(oi, 0.2),
#   "  0%" = oi,
#   " 20%" =  darken(oi, 0.2),
#   " 25%" =  darken(oi, 0.25),
#   " 30%" =  darken(oi, 0.3),
#   " 35%" =  darken(oi, 0.35),
#   off = c(0, 0)
# )

networks
## # A tibble: 13 x 7
##    NETWORK              NETWORKKEY   RED GREEN  BLUE ALPHA hex    
##    <chr>                     <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  
##  1 Visual1                       1     0     0   255   255 #0707CF
##  2 Visual2                       2   100     0   255   255 #5001D0
##  3 Somatomotor                   3     0   255   255   255 #11C7C7
##  4 Cingulo-Opercular             4   153     0   153   255 #7D007D
##  5 Dorsal-Attention              5     0   255     0   255 #10C710
##  6 Language                      6     0   155   155   255 #097A7A
##  7 Frontoparietal                7   255   255     0   255 #C7C70B
##  8 Auditory                      8   250    62   251   255 #D105D2
##  9 Default                       9   255     0     0   255 #CC0303
## 10 Posterior-Multimodal         10   177    89    40   255 #88492D
## 11 Ventral-Multimodal           11   255   157     0   255 #C97B05
## 12 Orbito-Affective             12    65   125     0   168 #336400
## 13 Subcortical                  13     0     0     0   255 #000000

get row and column designs

## match ROIs to networks
ROI.network.match <- cbind(spins_grads$ROI, spins_grads$Network) %>% unique
ROI.idx <- ROI.network.match[,2]
names(ROI.idx) <- ROI.network.match[,1]
### match networks with colors
net.col.idx <- networks$hex
names(net.col.idx) <- networks$NETWORK

## design matrix for subjects

diagnostic.col <- sub.dx$diagnostic_group %>% as.matrix %>% makeNominalData() %>% createColorVectorsByDesign()
rownames(diagnostic.col$gc) <- sub(".","", rownames(diagnostic.col$gc))

## design matrix for columns - behavioral 
behav.dx <- matrix(nrow = ncol(behav.dat), ncol = 1, dimnames = list(colnames(behav.dat), "type")) %>% as.data.frame

behav.col <- c("scog" = "#F28E2B",
               "np" = "#59A14F",
               "bsfs" = "#D37295",
               "bprs" = "#E15759",
               "qls" = "#B07AA1",
               "sans" = "#FF9888")

behav.dx$type <- sub("(^[^_]+).*", "\\1", colnames(behav.dat))
behav.dx$type.col <- recode(behav.dx$type, !!!behav.col)

## design matrix for columns - gradient
grad.dx <- matrix(nrow = ncol(grad.dat), ncol = 4, dimnames = list(colnames(grad.dat), c("gradient", "ROI", "network", "network.col"))) %>% as.data.frame

grad.dx$gradient <- sub("(^[^.]+).*", "\\1", colnames(grad.dat))
grad.dx$ROI <- sub("^[^.]+.(*)", "\\1", colnames(grad.dat))
grad.dx$network <- recode(grad.dx$ROI, !!!ROI.idx)
grad.dx$network.col <- recode(grad.dx$network, !!!net.col.idx)

## get different alpha for gradients
grad.col.idx <- c("grad1" = "grey30",
                  "grad2" = "grey60",
                  "grad3" = "grey90")
grad.dx$gradient.col <- recode(grad.dx$gradient, !!!grad.col.idx)

## for heatmap
col.heat <- colorRampPalette(c("red", "white", "blue"))(256)

Run PLS-C

pls.res <- tepPLS(behav.reg, grad.reg, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)
## [1] "DESIGN has too many columns or not enough elements. If the current DESIGN fails, a default will be created."
## [1] "DESIGN is not dummy-coded matrix. Creating default."
pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000, nf2keep = 5, eig = TRUE)
## Registered S3 method overwritten by 'data4PCCAR':
##   method                  from     
##   print.str_colorsOfMusic PTCA4CATA
pls.boot$bootRatiosSignificant.j[abs(pls.boot$bootRatios.j) < 2.75] <- FALSE
pls.boot$bootRatiosSignificant.i[abs(pls.boot$bootRatios.i) < 2.75] <- FALSE

pls.inf <- data4PCCAR::perm4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000)

## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1

## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs, 
          p.ev = pls.inf$pEigenvalues)

## Print singular values
pls.res$TExPosition.Data$pdq$Dv
##  [1] 6.3016043 2.9367859 2.6783153 2.0027332 1.8638835 1.7940189 1.7241155
##  [8] 1.5675356 1.5022984 1.3802431 1.2513969 1.1644155 1.0757232 0.9505441
## Print eigenvalues
pls.res$TExPosition.Data$eigs
##  [1] 39.7102170  8.6247116  7.1733727  4.0109403  3.4740618  3.2185038
##  [7]  2.9725742  2.4571678  2.2569006  1.9050711  1.5659943  1.3558634
## [13]  1.1571805  0.9035341
pls.res$TExPosition.Data$t
##  [1] 49.154768 10.675986  8.879465  4.964890  4.300322  3.983982  3.679562
##  [8]  3.041573  2.793675  2.358167  1.938445  1.678338  1.432401  1.118428
## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)
## [1] 90.88091
sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))
## [1] 0.005519977

Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.

Results

Dimension 1

lxly.out[[1]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
               threshold = 0, 
               color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")
## Warning: Vectorized input to `element_text()` is not officially supported.
## i Results may be unexpected or may change in future versions of ggplot2.
cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)

## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)

Dimension 2

lxly.out[[2]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
               threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"), 
               horizontal = FALSE, main = "Scores - behavioural")

dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))


cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)

Dimension 3

lxly.out[[3]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
               threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")

dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))


cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)

Dimension 4

lxly.out[[4]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
               threshold = 0, 
               color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")


dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))


cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)

Back into the brain

Dimension 1

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 2

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 3

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 4

## merging atlas and data by 'label'
## merging atlas and data by 'label'

Group difference and fancy figures

Cohen’s

## merging atlas and data by 'label'